Difference between revisions of "Weather Monitoring"

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{|class="collapsing_table collapsible collapsed"
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__NOTOC__
!'''Detailed information on other pages'''
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{{Non expert}}
|-
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==General description==
|
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[[File:Flowchart_mcyfs_modules_weather.jpg|thumb|right|200px|The role of weather monitoring within the MCYFS]]
*[[Meteorological data from ground stations condensed|Wheather observation data]]
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The weather monitoring module is one of the five modules of the MCYFS and can be split in four procedures.
*[[Interpolation method onto regular climatic grid|Interpolation to grid]]
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#[[#Acquisition|Acquisition]]
*[[Calculation of advanced parameters]]
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#[[#Interpolation|Interpolation]]
*[[Analysis of weather indicators|Spatial analyses]]
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#[[#Aggregation|Aggregation]]
*[[Time series analysis at station, grid and regional level|Time series analyses]]
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#[[#Climatology and analysis|Climatology and analysis]]
*[[Meteorological indicators from ecmwf model|ECMWF model data]]
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*[[Future developments: cgms numerical weather based|Future developments]]
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|}
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The output of the weather monitoring module is used in two ways: firstly, to derive agro-meteorological indicators for a direct evaluation of alarming situations such as drought, extreme rainfall during sowing, flowering or harvest etc., and secondly, as input to the [[Crop Simulation|crop simulation]] module to simulate crops behaviour and to evaluate the effect of weather on crops.
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==Acquisition==
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[[File:Weather_station.jpg|thumb|right|200px|Weather Station, Garreg Fawr, Aberdaron]]
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====Observed weather====
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Each day raw data of at least 4200 synoptic weather stations, that regularly collect and supply one or more meteorological variables, are acquired over Europe and its neighbourhood and are added as raw data to the station weather database. The variables collected include air temperature, precipitation, radiation, air humidity, and wind speed. All incoming data are checked for errors and dubious values, such as e.g. air temperatures outside a realistic range. The incoming 3- or 6-hourly data are then converted to daily values that fit in an uniform station weather database. Some variables, required for the crop simulation module, are not (or not regularly) observed. Such variables, for example solar radiation or evapotranspiration, are derived from the measured data and also added to the database.
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[[File:Supercomputers_at_ecmwf.jpg|thumb|right|200px|Supercomputer at ECMWF]]
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====Forecasted weather====
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In addition to observed weather data also weather forecast data are loaded into the system so that crop development and biomass accumulation can be simulated into the future (see [[Crop Simulation|crop simulation module]]), reaching closer to the end of the crop season and therefore advancing crop yield forecasts along the season (see [[Yield Forecasting|yield forecasting module]]).
  
==Introduction==
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Six data products from {{Gloshint|ECMWF|European Centre for Medium-Range Weather Forecasts. |ECMWF}} are loaded into the system:
{|
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*ERA-Interim (ERA)
|-valign="top"
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*Analysis model (HIS)
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*Deterministic forecast model (OPE)
The weather monitoring is one of the main elements of the system. It can be divided in three categories:
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*Ensemble Prediction System (ENS)
* data bases
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*Monthly forecast model (ENSEXT)
* data fluxes
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*Seasonal forecast model (SEAS)
* procedures
 
  
The Data bases store all the raw data and processed data. The data fluxes represent the flow of data from external sources to databases, between databases, towards others parts of the system and the end-users. The software procedures manipulate the data.
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These products have a different number of forecast days (forecast depth) and a varying number of possible weather realizations called 'members'. Different members can be thought of as model runs with a slightly different initialization and thus slightly different results, but with equal validity. Similar to observed weather, meteorological variables such as air temperature, precipitation, and solar radiation are directly retrieved from the forecast data. Additional parameters such as evapotranspiration are calculated from these variables within the MCYFS.
|[[image:architecture_weather_monitoring.jpg|250px|right]]
 
|}
 
  
==General description==
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{{Scientific_box_2|
{|
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*[[Meteorological data from ground stations]]
|-valign="top"
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*[[Meteorological data from ECMWF models]]
|
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}}
The meteorological data processing is managed by a sub-system called Crop Growth Monitoring System (CGMS). The database structure is presented in [[Appendix 4: CGMS DB description#Appendix 4|Appendix 4]]. Individual tables are described in [[Appendix 5: CGMS tables#Appendix 5|Appendix 5]]. Procedures may be stored as database objects, scripts or separate software packages. A detailed description can be found in [[Appendix 3: Overview of the software#Appendix 3|Appendix 3]].
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|[[image:flowchart_weather_monitoring.jpg|250px|right]]
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==Interpolation==
|}
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[[File:Interpolating_observed_weather.jpg‎‎|Interpolation from weather stations to  25 x 25 km regular climate grid.|thumb|200px]]
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====Observed weather====
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Observed weather, aquired from weather stations, have an irregular distribution and density in space. Data of a single weather station are representative for the location of that station only. To construct weather data for locations in between stations a conversion is needed. Interpolation (constructing new data points within the range of a discrete set of known data points) is one of the methods to do this. In the MCYFS this procedure is used to convert irregular distributed station data to regular distributed data. The regular distribution is organized as a grid with side by side grid cells of 25 kilometre width and 25 kilometre length that covers the entire region of interest (e.g. Europe). This is called the regular climatic grid. The interpolation is managed by a sub-system called {{Hint|CGMS|Crop Growth Monitoring System}}.
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[[File:Downscalling_eps_mon.jpg‎|Interpolation from 0.5 x 0.5 degrees grid to  25 x 25 km regular climate grid.|thumb|200px]]
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 +
====Forecasted weather====
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Weather forecast data come already as spatial fields, not point data as observations, but in varying spatial resolutions and projections that are different from the regular climatic grid used in the MCYFS. Therefore, forecast data have to be interpolated from the 'source' grid to the 'target' grid: the regular climatic grid of 25 by 25 km. This specific interpolation procedure is also called "downscaling", because usually it converts a low resolution source data into higher resolution target data.
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{{Scientific_box_2|
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*[[Interpolation of observed weather]]
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*[[Interpolation of forecasted weather]]
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}}
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==Aggregation==
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[[File:Aggregation_to_regions.jpg|Example of four different administrative levels in combination with landcover type 'arable land' on the 25 km grid.|thumb|200px]]
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The interpolation procedure generates gridded datasets of observed and forecast weather. In order to describe weather at a spatial domain larger than the grid size and to answer questions like e.g.:
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''''What was the average temperature in northern France during the last week for locations where winter wheat is grown?''''
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gridded weather data are aggregated to different types of regions. Observed and forecast gridded weather data are aggregated to different levels of administrative regions for a number of landcover types. This aggregation is based on a weight of each grid cell for the area covered by the selected landcover type.
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Altogether, the aggregation procedure results in many aggregated weather data sets based on the data type (e.g. observed, ERA-Interim, forecast), regions and landcover types.
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{{Scientific_box_2|[[Aggregation of weather indicators]]}}
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==Climatology and analysis==
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[[File:Climatology.jpg|Long-term average air temperature over period January-June on a 25 km resolution.|thumb|200px]]
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Within the MCYFS climatology is considered as the long-term average of weather indicators. The long-term averages - or 'normal' conditions - are essential to understand how current weather conditions relate to the situation that was 'normal' in the past.
 +
 
 +
Long-term average daily values are calculated over the years in the archive for all spatial resolutions defined in each regional window. Two periods are considered: 1975-last year and 1995-last year.
 +
 
 +
In addition to averages of the basic indicators (such as precipitation or air temperature) additional statistics are calculated, for instance the probability of having a rainy day, defined as a day receiving more than a certain amount of rainfall (5, 10, 15 mm). It allows to compare extreme weather events of the current year to extremes of the past. For example, the number of rainy days (with more than 5 mm/d) of last month can be compared to the average number of rainy days of this month that occurred in the past.
  
==Goals and assumptions==
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{{Scientific_box_2|
Daily meteorological station data are used in two ways for crop yield evaluations. In the first place as input for the crop growth model WOFOST to simulate crops behaviors and evaluate the effects of weather on crops yields at European level (see [[Crop Simulation]]). Secondly as weather indicators for a direct evaluation of alarming situations such as drought, extreme rainfall during sowing, flowering or harvest etc.<br><br>
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* [[Calculation of Climatology]]
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* [[Analysis of weather indicators]]
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}}
  
The crops behaviors are mainly influenced by the atmospheric conditions near the earth surface. Considering the data availability, resources and purpose of the system a time scale of one day and a spatial scale of 25 by 25 km are chosen as the resolutions to estimate crop yields at European scale.
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[[Category:MCYFS introduction]]
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[[Category:Weather Monitoring]]

Latest revision as of 13:51, 13 August 2018



General description

The role of weather monitoring within the MCYFS

The weather monitoring module is one of the five modules of the MCYFS and can be split in four procedures.

  1. Acquisition
  2. Interpolation
  3. Aggregation
  4. Climatology and analysis


The output of the weather monitoring module is used in two ways: firstly, to derive agro-meteorological indicators for a direct evaluation of alarming situations such as drought, extreme rainfall during sowing, flowering or harvest etc., and secondly, as input to the crop simulation module to simulate crops behaviour and to evaluate the effect of weather on crops.

Acquisition

Weather Station, Garreg Fawr, Aberdaron

Observed weather

Each day raw data of at least 4200 synoptic weather stations, that regularly collect and supply one or more meteorological variables, are acquired over Europe and its neighbourhood and are added as raw data to the station weather database. The variables collected include air temperature, precipitation, radiation, air humidity, and wind speed. All incoming data are checked for errors and dubious values, such as e.g. air temperatures outside a realistic range. The incoming 3- or 6-hourly data are then converted to daily values that fit in an uniform station weather database. Some variables, required for the crop simulation module, are not (or not regularly) observed. Such variables, for example solar radiation or evapotranspiration, are derived from the measured data and also added to the database.

Supercomputer at ECMWF

Forecasted weather

In addition to observed weather data also weather forecast data are loaded into the system so that crop development and biomass accumulation can be simulated into the future (see crop simulation module), reaching closer to the end of the crop season and therefore advancing crop yield forecasts along the season (see yield forecasting module).

Six data products from ECMWF are loaded into the system:

  • ERA-Interim (ERA)
  • Analysis model (HIS)
  • Deterministic forecast model (OPE)
  • Ensemble Prediction System (ENS)
  • Monthly forecast model (ENSEXT)
  • Seasonal forecast model (SEAS)

These products have a different number of forecast days (forecast depth) and a varying number of possible weather realizations called 'members'. Different members can be thought of as model runs with a slightly different initialization and thus slightly different results, but with equal validity. Similar to observed weather, meteorological variables such as air temperature, precipitation, and solar radiation are directly retrieved from the forecast data. Additional parameters such as evapotranspiration are calculated from these variables within the MCYFS.


Interpolation

Interpolation from weather stations to 25 x 25 km regular climate grid.

Observed weather

Observed weather, aquired from weather stations, have an irregular distribution and density in space. Data of a single weather station are representative for the location of that station only. To construct weather data for locations in between stations a conversion is needed. Interpolation (constructing new data points within the range of a discrete set of known data points) is one of the methods to do this. In the MCYFS this procedure is used to convert irregular distributed station data to regular distributed data. The regular distribution is organized as a grid with side by side grid cells of 25 kilometre width and 25 kilometre length that covers the entire region of interest (e.g. Europe). This is called the regular climatic grid. The interpolation is managed by a sub-system called CGMS.

Interpolation from 0.5 x 0.5 degrees grid to 25 x 25 km regular climate grid.

Forecasted weather

Weather forecast data come already as spatial fields, not point data as observations, but in varying spatial resolutions and projections that are different from the regular climatic grid used in the MCYFS. Therefore, forecast data have to be interpolated from the 'source' grid to the 'target' grid: the regular climatic grid of 25 by 25 km. This specific interpolation procedure is also called "downscaling", because usually it converts a low resolution source data into higher resolution target data.


Aggregation

Example of four different administrative levels in combination with landcover type 'arable land' on the 25 km grid.

The interpolation procedure generates gridded datasets of observed and forecast weather. In order to describe weather at a spatial domain larger than the grid size and to answer questions like e.g.:

'What was the average temperature in northern France during the last week for locations where winter wheat is grown?'

gridded weather data are aggregated to different types of regions. Observed and forecast gridded weather data are aggregated to different levels of administrative regions for a number of landcover types. This aggregation is based on a weight of each grid cell for the area covered by the selected landcover type.

Altogether, the aggregation procedure results in many aggregated weather data sets based on the data type (e.g. observed, ERA-Interim, forecast), regions and landcover types.


Climatology and analysis

Long-term average air temperature over period January-June on a 25 km resolution.

Within the MCYFS climatology is considered as the long-term average of weather indicators. The long-term averages - or 'normal' conditions - are essential to understand how current weather conditions relate to the situation that was 'normal' in the past.

Long-term average daily values are calculated over the years in the archive for all spatial resolutions defined in each regional window. Two periods are considered: 1975-last year and 1995-last year.

In addition to averages of the basic indicators (such as precipitation or air temperature) additional statistics are calculated, for instance the probability of having a rainy day, defined as a day receiving more than a certain amount of rainfall (5, 10, 15 mm). It allows to compare extreme weather events of the current year to extremes of the past. For example, the number of rainy days (with more than 5 mm/d) of last month can be compared to the average number of rainy days of this month that occurred in the past.